Business Problem

Over the past few years, Airbnb has witnessed a dramatic increase in the volume of transactions on it’s site. Simulataneously, it’s customer experience team has been overwhelmed by a surge in requests for help across a number of languages. Focusing on French service requests, this report will analyze support requests in relation to transactions, build a forcasting model to predict future request volume, and finally make recommendations on the amount of resources needed to handle that volume based on historical data.

The below shows that French is among the top ranking languages in number of support request volume over time with English being the top ranked language overall. English has been removed from the below plot to better compare French language requests with others. Feel free to use the input selection to compare daily support requests over time for other languages in relation to French. A seperate color can be selected in the brush color option to differentiate.

Transactions vs Support Requests

While exploring the data I plotted both booking and checkin daily totals against support request totals on that same day. I also fit linear models to each variable to better see their relationships by interpretting their results. We can see that both bookings and checkins have significant P values and bookings have a significantly higher R-squared value than checkins. One hypothesis that can be formed from this is that customers require more support when booking an AirBnb as opposed to checking into one.

Prophet Forecast Model

To create a forcast model for support requests I used the Facebook developed Prophet forcasting package. There are many methods for modeling time series data but this package was chosen due to its ease of use and ability to accurately model time series data containing multiple period trends. More information on this package can be found here.

My methodology for training and testing my model on daily support request totals is as follows:

  • Create a test and train set from the given data.
  • Fit the model on the train dataset and forcast request totals for the test dataset.
  • Measure accuracy of the model by calculating the root-mean-square error (RMSE).
  • Repeat steps with different tuning perameters and with regressors added from the transactional data set to determine optimal setup.

Surprisingly the Prohpet model without the booking and checkin totals added as regressors performed best. Below are the predicted daily support values plotted alongside the actual values. We can see the model predicts very close to the actual support request totals.

Future Predictions

Using our Prophet model we can predict support request totals for the future April through December period. Below are the prediction results appended to the end of the original known support data.

Resource Allocation

In order to get a picture of Airbnb’s historical French speaking support staff totals in relation to daily requests I looked at requests per asignee over time. After calculating support request totals and the number of asignees handling those requests per day I was able to divide the 2 to come up with a target ratio. Below is the ratio smoothed and plotted over time for the period given.

Business Assumptions

Since the support team has been recently overwhelmed with the amount of requests coming in I will made the assumption that the 1.7 requests per resouce from the beginning of the dataset was not an issue for the support team’s capacity. I will also assume that once the ratio rises above this level in March and April is when the customer experience team started becoming overwhelmed. Based on the assumptions we will set our ideal request to support staff ratio at 1.7 for the future.

Final Conclusion

With these assumptions in mind I took the predicted support request values, averaged them by week, and divided them by 1.7 to get the number of French speaking resources needed for each week remaining in the year. Below is a graph representing these projections and a table containing the raw values for the call center management team to use when scaling their team.

Weekly Resources needed for French Support Requests
week average_requests resources_needed
14 189.4758 111
15 199.6282 117
16 203.3966 120
17 202.5220 119
18 202.9858 119
19 208.1313 122
20 214.6137 126
21 216.7916 128
22 214.5977 126
23 214.6799 126
24 222.3543 131
25 233.9524 138
26 240.0505 141
27 237.2265 140
28 234.3577 138
29 244.2021 144
30 268.0998 158
31 291.2157 171
32 295.3780 174
33 276.9817 163
34 250.9759 148
35 236.5504 139
36 239.6261 141
37 249.9295 147
38 253.9324 149
39 248.4085 146
40 240.7680 142
41 238.3457 140
42 240.2242 141
43 240.0068 141
44 234.3103 138
45 226.2462 133
46 220.8266 130
47 219.1477 129
48 218.4002 128
49 216.5318 127
50 214.9505 126
51 216.2241 127
52 220.5202 130